It’s been three weeks since Strategic Reading was last updated and the last weekly summary sent out to email subscribers. If you had noticed the void in your reading life, apologies. As is well known, strategists are not always as gifted in predicting the future as they like to think, but the intention is now for normal service to be resumed.
Dave Briggs is starting a new weekly newsletter. It’s a pretty safe bet that if you like this, you’ll like that.
Am rebooting my email newsletter. Sign up for it here: https://t.co/MaYsiL4Hs0
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— Dave Briggs (@davebriggs) June 2, 2018
Tim Harford recommends some books about algorithms. There’s not much more to be said than that – except perhaps to follow up on one of the implications of Prediction Machines, the book which is the main focus of the post.
One way of looking at artificial intelligence is as a tool for making predictions. Good predictions reduce uncertainty. Really good predictions may change the nature of a problem altogether. In a different sense, the purpose of strategy can also be seen as a way of reducing uncertainty: by making some choices (or bets), other choices drop out of the problem space. Putting those two thoughts together suggests that better AI may be a tool to support better strategies.
If you had to write down a list of innovation methods and techniques, how many could you come up with? However long your list, it’s a fair bit that it won’t have as much on it as this landscape of innovation approaches (also available as a more legible PDF to cut out and keep).
Methods are grouped into four overlapping ‘spaces’. There’s room for debate about what best fits where and there is a broad range from mainstream to eclectic – but that in itself is a good start in challenging assumptions about methods which appear natural and obvious and indeed about the kind of innovation being sought.
How many design innovation toolkits are there? The answer seems to be that there are more than you might think possible. Over a hundred are brought together on this page, which makes it an extraordinarily rich collection. There are lots of interesting-looking things here, some well known, others more obscure – though it’s hard not to come away with the thought that the world’s need for innovation toolkits has now over abundantly been met.
Dave reads and reflects and shares both the reading and the reflections, on topics which are often closely linked to themes covered here. He has just announced a slightly different approach to sharing the material he finds, including a dedicated category on his blog (which comes with a selective RSS feed). Well worth following – though there is no obvious reason to filter out his own posts, which are always worth reading in their own right.
This is a page of links which provides over two hundred examples of artificial intelligence in action – ranging from mowing the lawn, through managing hedge funds and sorting cucumbers all the way to writing AI software. Without clicking a single one of the links, it provides a powerful visual indicator of how pervasive AI has already become. There is inevitably a bit of a sense of never mind the quality, feel the width – but the width is itself impressive, and the quality is often racing up as well.
There is a linked twitter account which retweets AI-related material – though in a pleasing inversion, it shows every sign of being human-curated.
Including this link is slightly indulgently self-referential, for reasons which will be apparent to anybody who reads the last fifth of this article – but there is value in the first four fifths regardless of that. More generally, aggregators of good things from across the web are to be welcomed, and others will get mentioned here from time to time. As Benedict Evans once put it, “All curation grows until it requires search. All search grows until it requires curation.” This is where curation is celebrated.